Data Engineering Manager

Diagonal recruitment
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Data Engineering Manager

BDO Digital Data Architecture and Engineering Senior Manager/Director

Data Science Manager – Property Tech – London

BI and Data Engineering Lead

BI and Data Engineering Lead

BI and Data Engineering Lead

Job Description

*our client is unable to offer sponsorship - only apply if you have the right to work in the UK


Overview


Our client is a well-funded startup (c200 people) and readying to scale. They have award-winning solutions in the fast-moving digital advertising space with petabyte scale proprietary data.


The Role


We're seeking a hands-on Data Engineering Manager to lead a team of Data Engineers and work alongside the Head of Engineering and Principal Data Architect and the Product team.


You will join a Product, Data and Engineering org of 50+ people based out of London and NYC and will create the standard for the data practice and ensure best in class design and delivery, working across multiple products covering audience insights, analytics and advertising.


You'll be relied upon to get the data stack AI ready, drive innovation and leverage Agentic development where its needed.


Technology / Skills requirements


  • SQL and another language e.g. Python
  • ELT/ETL
  • CI/CD
  • Data Modelling
  • Data Architecture
  • Data Frameworks
  • dbt
  • Data processing and orchestration using Airflow or similar
  • Google BigQuery or Redshift
  • Cloud based services: GCP (preferred) or...

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Careers

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

Jobs

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

Education

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.